Init version of LaBSE-kbd model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

2_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:992990d6f469e8332310d44a3874c4a1cc7a06a188b54a4060e97e345cf97c32
+size 2362528

README.md

@@ -0,0 +1,521 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3395988
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/LaBSE
+widget:
+- source_sentence: Tom grabbed Mary's elbow.
+  sentences:
+  - Tom, Mary'yi dirseğinden kavradı.
+  - Stay with her.
+  - Pourquoi a-t-il mangé l'abeille ?
+- source_sentence: Жизнь - это тень.
+  sentences:
+  - Life is a shadow.
+  - I'm almost always at home on Sundays.
+  - Henüz bir vizem yok.
+- source_sentence: Are you working tomorrow?
+  sentences:
+  - Yarın çalışacak mısın?
+  - Нобэ хуабей дыдэт.
+  - Мэри къэшэн имыIэну жеIэ.
+- source_sentence: Вы нарушили закон.
+  sentences:
+  - Ахэр Iейщ.
+  - Tom war drei Tage nicht da.
+  - Vous avez enfreint la loi.
+- source_sentence: We've never seen Tom this angry before.
+  sentences:
+  - Tom'u daha önce asla bu kadar öfkeli görmedik.
+  - Soyez attentive aux voleurs à la tire.
+  - Endişeli görünüyorsun.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on sentence-transformers/LaBSE
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: validation
+      type: validation
+    metrics:
+    - type: pearson_cosine
+      value: -0.2799955028525394
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: -0.32115994644018286
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on sentence-transformers/LaBSE
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision b7f947194ceae0ddf90bafe213722569e274ad28 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **HF中国镜像站:** [Sentence Transformers on HF中国镜像站](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  (3): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("panagoa/LaBSE-kbd-v0.2")
+# Run inference
+sentences = [
+    "We've never seen Tom this angry before.",
+    "Tom'u daha önce asla bu kadar öfkeli görmedik.",
+    'Soyez attentive aux voleurs à la tire.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Dataset: `validation`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value       |
+|:--------------------|:------------|
+| pearson_cosine      | -0.28       |
+| **spearman_cosine** | **-0.3212** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 3,395,988 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                           |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 10.33 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.81 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.36</li><li>max: 0.98</li></ul> |
+* Samples:
+  | sentence_0                             | sentence_1                                  | label                           |
+  |:---------------------------------------|:--------------------------------------------|:--------------------------------|
+  | <code>Почему вас это удивило?</code>   | <code>Сыт ар щIывгъэщIэгъуар?</code>        | <code>0.9298050403594972</code> |
+  | <code>Ребёнка кто-нибудь видел?</code> | <code>Quelqu'un a-t-il vu l'enfant ?</code> | <code>0.0</code>                |
+  | <code>Marie se couchait.</code>        | <code>Мэри гъуэлъырт.</code>                | <code>0.9330472946166992</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 2
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | validation_spearman_cosine |
+|:------:|:-----:|:-------------:|:--------------------------:|
+| 0.0005 | 100   | -             | -0.7761                    |
+| 0.0009 | 200   | -             | -0.7598                    |
+| 0.0014 | 300   | -             | -0.7485                    |
+| 0.0019 | 400   | -             | -0.7412                    |
+| 0.0024 | 500   | 0.2864        | -0.7354                    |
+| 0.0028 | 600   | -             | -0.7307                    |
+| 0.0033 | 700   | -             | -0.7191                    |
+| 0.0038 | 800   | -             | -0.7206                    |
+| 0.0042 | 900   | -             | -0.7197                    |
+| 0.0047 | 1000  | 0.0463        | -0.7037                    |
+| 0.0052 | 1100  | -             | -0.6866                    |
+| 0.0057 | 1200  | -             | -0.6798                    |
+| 0.0061 | 1300  | -             | -0.6844                    |
+| 0.0066 | 1400  | -             | -0.6716                    |
+| 0.0071 | 1500  | 0.0184        | -0.6658                    |
+| 0.0075 | 1600  | -             | -0.6620                    |
+| 0.0080 | 1700  | -             | -0.6532                    |
+| 0.0085 | 1800  | -             | -0.6455                    |
+| 0.0090 | 1900  | -             | -0.6452                    |
+| 0.0094 | 2000  | 0.011         | -0.6360                    |
+| 0.0099 | 2100  | -             | -0.6240                    |
+| 0.0104 | 2200  | -             | -0.6220                    |
+| 0.0108 | 2300  | -             | -0.6294                    |
+| 0.0113 | 2400  | -             | -0.6038                    |
+| 0.0118 | 2500  | 0.0092        | -0.6116                    |
+| 0.0122 | 2600  | -             | -0.5996                    |
+| 0.0127 | 2700  | -             | -0.6120                    |
+| 0.0132 | 2800  | -             | -0.5940                    |
+| 0.0137 | 2900  | -             | -0.5848                    |
+| 0.0141 | 3000  | 0.0071        | -0.5958                    |
+| 0.0146 | 3100  | -             | -0.5840                    |
+| 0.0151 | 3200  | -             | -0.5944                    |
+| 0.0155 | 3300  | -             | -0.5895                    |
+| 0.0160 | 3400  | -             | -0.5849                    |
+| 0.0165 | 3500  | 0.0056        | -0.5708                    |
+| 0.0005 | 100   | -             | -0.5686                    |
+| 0.0009 | 200   | -             | -0.5608                    |
+| 0.0014 | 300   | -             | -0.5587                    |
+| 0.0024 | 500   | 0.0053        | -                          |
+| 0.0047 | 1000  | 0.0081        | -0.5882                    |
+| 0.0071 | 1500  | 0.0058        | -                          |
+| 0.0094 | 2000  | 0.0064        | -0.5127                    |
+| 0.0118 | 2500  | 0.004         | -                          |
+| 0.0141 | 3000  | 0.0042        | -0.4934                    |
+| 0.0165 | 3500  | 0.0048        | -                          |
+| 0.0188 | 4000  | 0.0036        | -0.4762                    |
+| 0.0212 | 4500  | 0.0051        | -                          |
+| 0.0236 | 5000  | 0.0054        | -0.4754                    |
+| 0.0259 | 5500  | 0.0054        | -                          |
+| 0.0283 | 6000  | 0.0054        | -0.4609                    |
+| 0.0306 | 6500  | 0.0044        | -                          |
+| 0.0330 | 7000  | 0.0048        | -0.4716                    |
+| 0.0353 | 7500  | 0.0061        | -                          |
+| 0.0377 | 8000  | 0.0018        | -0.4293                    |
+| 0.0400 | 8500  | 0.0047        | -                          |
+| 0.0424 | 9000  | 0.0043        | -0.4311                    |
+| 0.0448 | 9500  | 0.0034        | -                          |
+| 0.0471 | 10000 | 0.0041        | -0.4429                    |
+| 0.0495 | 10500 | 0.0028        | -                          |
+| 0.0518 | 11000 | 0.0032        | -0.4324                    |
+| 0.0542 | 11500 | 0.0025        | -                          |
+| 0.0565 | 12000 | 0.0037        | -0.4374                    |
+| 0.0589 | 12500 | 0.003         | -                          |
+| 0.0612 | 13000 | 0.005         | -0.4522                    |
+| 0.0636 | 13500 | 0.0051        | -                          |
+| 0.0660 | 14000 | 0.0048        | -0.3994                    |
+| 0.0683 | 14500 | 0.0034        | -                          |
+| 0.0707 | 15000 | 0.0032        | -0.4148                    |
+| 0.0730 | 15500 | 0.0046        | -                          |
+| 0.0754 | 16000 | 0.0026        | -0.3848                    |
+| 0.0777 | 16500 | 0.0036        | -                          |
+| 0.0801 | 17000 | 0.0051        | -0.3845                    |
+| 0.0824 | 17500 | 0.0031        | -                          |
+| 0.0848 | 18000 | 0.0035        | -0.3500                    |
+| 0.0872 | 18500 | 0.0028        | -                          |
+| 0.0895 | 19000 | 0.0021        | -0.3634                    |
+| 0.0919 | 19500 | 0.0025        | -                          |
+| 0.0942 | 20000 | 0.0023        | -0.3428                    |
+| 0.0966 | 20500 | 0.0042        | -                          |
+| 0.0989 | 21000 | 0.0038        | -0.3432                    |
+| 0.1013 | 21500 | 0.005         | -                          |
+| 0.1037 | 22000 | 0.0024        | -0.3515                    |
+| 0.1060 | 22500 | 0.0029        | -                          |
+| 0.1084 | 23000 | 0.0033        | -0.3929                    |
+| 0.1107 | 23500 | 0.003         | -                          |
+| 0.1131 | 24000 | 0.0029        | -0.3309                    |
+| 0.1154 | 24500 | 0.0038        | -                          |
+| 0.1178 | 25000 | 0.0028        | -0.3369                    |
+| 0.1201 | 25500 | 0.0025        | -                          |
+| 0.1225 | 26000 | 0.002         | -0.3257                    |
+| 0.1249 | 26500 | 0.0025        | -                          |
+| 0.1272 | 27000 | 0.0033        | -0.3659                    |
+| 0.1296 | 27500 | 0.0023        | -                          |
+| 0.1319 | 28000 | 0.0031        | -0.3208                    |
+| 0.1343 | 28500 | 0.0027        | -                          |
+| 0.1366 | 29000 | 0.0031        | -0.3298                    |
+| 0.1390 | 29500 | 0.0047        | -                          |
+| 0.1413 | 30000 | 0.003         | -0.3460                    |
+| 0.1437 | 30500 | 0.004         | -                          |
+| 0.1461 | 31000 | 0.0027        | -0.3567                    |
+| 0.1484 | 31500 | 0.0063        | -                          |
+| 0.1508 | 32000 | 0.003         | -0.3382                    |
+| 0.1531 | 32500 | 0.0022        | -                          |
+| 0.1555 | 33000 | 0.0048        | -0.3475                    |
+| 0.1578 | 33500 | 0.0021        | -                          |
+| 0.1602 | 34000 | 0.0043        | -0.3323                    |
+| 0.1625 | 34500 | 0.0031        | -                          |
+| 0.1649 | 35000 | 0.0024        | -0.3207                    |
+| 0.1673 | 35500 | 0.0029        | -                          |
+| 0.1696 | 36000 | 0.0032        | -0.3004                    |
+| 0.1720 | 36500 | 0.0046        | -                          |
+| 0.1743 | 37000 | 0.0033        | -0.3085                    |
+| 0.1767 | 37500 | 0.002         | -                          |
+| 0.1790 | 38000 | 0.0022        | -0.3270                    |
+| 0.1814 | 38500 | 0.0036        | -                          |
+| 0.1837 | 39000 | 0.0034        | -0.3042                    |
+| 0.1861 | 39500 | 0.0034        | -                          |
+| 0.1885 | 40000 | 0.0016        | -0.3193                    |
+| 0.1908 | 40500 | 0.0026        | -                          |
+| 0.1932 | 41000 | 0.0028        | -0.2945                    |
+| 0.1955 | 41500 | 0.0031        | -                          |
+| 0.1979 | 42000 | 0.0016        | -0.2942                    |
+| 0.2002 | 42500 | 0.0021        | -                          |
+| 0.2026 | 43000 | 0.003         | -0.2998                    |
+| 0.2049 | 43500 | 0.0042        | -                          |
+| 0.2073 | 44000 | 0.0023        | -0.3245                    |
+| 0.2097 | 44500 | 0.0018        | -                          |
+| 0.2120 | 45000 | 0.0021        | -0.3212                    |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "sentence-transformers/LaBSE",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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config_sentence_transformers.json

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modules.json

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sentence_bert_config.json

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special_tokens_map.json

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tokenizer_config.json

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